Mouse Work

weights |>
  janitor::remove_empty() |> # Drops columns/rows with only NA values
  select(-`Min Weight`) |> # For this we just want the daily weights
  mutate_if(is.numeric,
            round,
            digits = 1) |>
  create_dt()

# Tidy and pivot into long form
cleanDF <-
  weights |>
  janitor::remove_empty() |> # Drops columns/rows with only NA values
  select(-`Min Weight`) |> # For this we just want the daily weights
  pivot_longer(cols = where(is.double),
               names_to = "Time",
               values_to = "g") |>
  mutate_at("Time", as.numeric) |>
  drop_na(g) |> # remove rows with no weigh (e.g mouse is dead or didn't weigh that day)
  janitor::clean_names() 

cleanDF |>
  mutate_if(is.numeric,
            round,
            digits = 1) |>
  create_dt()

raw_weight_plot <-
  cleanDF |>
  group_by(group, sex, time) |>
  ggplot(aes(x=time, y=g, group = mouse, colour = sex)) +
  geom_line(size = 1, alpha = 0.8)+
  labs(title = "Weight Change by mouse",
       subtitle = "<span style='color:#AD0000;'>Male</span> and <span style='color:#8B9DA1;'>Female</span> mice",
       caption = paste0("(Figure generated on ", Sys.Date(), ")")) +
  xlab("Time in days") +
  ylab("Weight (g)") +
  scale_x_continuous(breaks= pretty_breaks()) +
  facet_wrap(.~group, ncol = 2) + 
  #lab_style() +
  theme(legend.position = "none",
        plot.subtitle = element_markdown()) +
  NULL

# clean up dataframe

clinical <-
  clinical |>
  janitor::clean_names() |>
  na.omit()# Drops rows with NA values
  
clinical |>
  create_dt()

clin_score <- 
  clinical |>
  group_by(group, mouse, day) |>
  summarise(total_score = sum(score), .groups = "drop") |>
  ggplot(aes(x=day, y=total_score)) +
  geom_beeswarm(groupOnX=TRUE) + # from ggbeeswarm package - lays points out side by side instead of overlapping
  stat_summary(aes(y = total_score, x = day, group=1), fun=median, colour="red", geom="line", group=1) +
  scale_x_continuous(breaks= pretty_breaks()) +
  labs(title = "Clinical Score", 
       subtitle = "Points represent individual mice, line is the median score",
       caption = paste0("(Figure generated on ", Sys.Date(), ")")) +
  xlab("Time in days") +
  ylab("Clinical Score") +
  facet_wrap(.~group, nrow = 2) + 
  #lab_style() + 
  NULL

clin_score

clin_score2 <-
  clinical |>
  group_by(day) |>
  filter(sum(score, na.rm = TRUE) > 0) |> # Drop days that only contain 0 scores
  ggplot() +
  geom_bar(aes(x=day, y=score, fill=category), stat = "identity") +
  scale_y_continuous(expand = c(0, 0)) +
  facet_wrap(.~mouse) + 
  labs(title = "Clinical Score", 
       subtitle = "Individual Clinical Scores for <span style = 'color:#004E74;'>Diarrhea</span>, <span style = 'color:#8B9DA1;'>Activity</span>, <span style = 'color:#AD0000;'>Coat</span>, <span style = 'color:#00A89D;'>Posture</span>, and <span style = 'color:#FEBE10;'>Eyes & Nose</span>.<br>Only days that contain a score > 0 for at least one mouse are shown <br>(e.g. days with no disease signes are dropped)",
       caption = paste0("(Figure generated on ", Sys.Date(), ")")) +
  xlab("Time in days") +
  ylab("Clinical Scores") +
  #lab_style() +
  theme(legend.position = "none",
        plot.subtitle = element_markdown(),
        axis.text.y = element_text(size = 6),
        strip.text = element_text(size = 8)) +
  NULL

clin_score2

# Tidy and pivot into long form
cleanDF_cfu <- 
  cfu |>
    janitor::remove_empty() |># Drops columns/rows with only NA values
    pivot_longer(cols = where(is.double), names_to = "Time", values_to = "cfu") |>
    mutate_at("Time", as.numeric) |>
    janitor::clean_names()

cleanDF_cfu |>
  create_dt()

# Tidy and pivot into long form
cleanDF_stool <- 
  stool |>
    janitor::remove_empty() |># Drops columns/rows with only NA values
    pivot_longer(cols = where(is.double), names_to = "Time", values_to = "stool_weight") |>
    mutate_at("Time", as.numeric) |>
    janitor::clean_names() |>
    filter(weight_mg == "Stool") |>
    select(-weight_mg)

cleanDF_stool |>
  create_dt() 

cleanDF_cfu_gram <-
  full_join(cleanDF_cfu, cleanDF_stool) |>
  mutate(cfu_per_gram = (cfu/stool_weight)*1000)

cleanDF_cfu_gram |>
  create_dt() |>
  formatSignif(columns = c('cfu', 'cfu_per_gram'), digits = 3)

cfu_nums <- 
  cleanDF_cfu_gram |>
  na.omit() |>
  filter(morphotype != "Total") |># look at spores and veg individualy
  ggplot() +
  geom_bar(aes(x=time, y=log10(cfu_per_gram), fill = morphotype), stat = "identity", position = "dodge") +
  facet_wrap(.~mouse) + 
  #lab_style() + 
  scale_y_continuous(expand = c(0, 0), breaks= pretty_breaks()) +
  labs(title = "Bacterial load", 
       subtitle = "CFUs per gram from <span style = 'color:#8B9DA1;'>spores</span> and <span style = 'color:#AD0000;'>vegetative</span> cells",
       caption = paste0("(Figure generated on ", Sys.Date(), ")")) +
  ylab("log10 CFUs per gram") +
  xlab("Time (days)") +
  theme(legend.position = "none",
        plot.subtitle = element_markdown(),
        axis.text.y = element_text(size = 6),
        strip.text = element_text(size = 8)
        ) +
  NULL

cfu_nums

cfu_nums2 <- 
  cleanDF_cfu_gram |>
  na.omit() |>
  filter(morphotype == "Total") |>
  ggplot(aes(x=time, y=log10(cfu_per_gram), group = group, colour = group)) +
  geom_point(position = position_dodge(width = 0.95), alpha = 0.75) +
  stat_summary(geom = "crossbar", fun = mean, 
               width = 0.8, position = position_dodge(width = 0.95)) +
  #lab_style() +
  scale_y_continuous(breaks= pretty_breaks()) +
  scale_x_continuous(breaks= pretty_breaks()) +
  labs(title = "Bacterial load",
       subtitle = "Total CFUs per gram") +
  ylab("log10 CFUs per gram") +
  xlab("Time (days)") +
  theme(panel.grid.major.y = element_line(color="#cbcbcb")) + 
  NULL

cfu_nums2

# Statistical test

stat.test <- 
  cleanDF_cfu_gram |>
  na.omit() |> # remove rows without data
  group_by(time) |>
  anova_test(cfu_per_gram~group) |># from rstatix package
  adjust_pvalue(method = "holm") |>
  add_significance() 

stat.test |>
  create_dt()

exp_time <- max(surv$Time)

surv_plot <- 
  surv |>
  mutate(
    name_lab = if_else(Time == max(Time), Group, NA_character_)
  ) |>
  ggplot(aes(x=Time, y=`S(t)`, colour = Group)) +
  geom_step(size = 1.5, alpha = 0.8) +
  scale_y_continuous(breaks= pretty_breaks(), 
                     labels = scales::percent, 
                     limits = c(0,1), # set limits to 0-1 regardless of data
                     expand = expansion(mult = c(0, 0.05))) +
  scale_x_continuous(breaks= pretty_breaks(), 
                     expand = expansion(add  = c(0, 3))) + # expands x axis by 3 to make room for names
  labs(title = "Mouse Survival") +
  xlab("Time (days)") +
  ylab("") +
  geom_text_repel(
    aes(color = Group, label = name_lab),
    size = 5,
    direction = "y",
    xlim = c((exp_time+0.8), NA),
    hjust = 0,
    segment.size = .75,
    segment.alpha = .75,
    segment.linetype = "dotted",
    box.padding = .4,
    segment.curvature = -0.1,
    segment.ncp = 3,
    segment.angle = 20
  ) +
  coord_cartesian(
    clip = "off"
  ) +
  #lab_style() +
  theme(legend.position = "none") +
  NULL

surv_plot

pander(sessionInfo(), compact = FALSE)